- Motivation for this study
- Upwelling patterns
- Common types of unsupervised image classification
- PCA
- K-means
- Hierarchical clustering
- Seasonal patterns of upwelling revealed by image classification
- Changes in upwelling in the SEAS
9 Apr 2021 SAFS Quantative Seminar
2014-2019 NOAA Fisheries/India Ministry of Earth Sciences joint research on improving forecasts of the indian oil sardine
Improving forecasts using environmental covariates: a case study on the Indian oil sardine (Sardinella longiceps), May 24th, UW Fish and Wildlife Ecology Seminars.
Southwest coast of India off Kerala state. 350 miles of coastline. Disperse non-motorized and motorized fishery.
* Wind and rain
* Strong upwelling starts from the tip and moves north
Land warms faster than the ocean -> changes in coastal winds
Projected warming between 2015 and 2050
Di Lorenzo, E. 2015. The future of coastal ocean upwelling. Nature 518, 310–311.
Example of SST differential during strong upwelling period
…as opposed to other metrics of upwelling intensity.
Here are 5 images and just the first 10 pixels of the image.
## p1 p2 p3 p4 p5 p6 p7 p8 p9 p10 ## 1979-01-01 0.11 0.01 -0.08 -0.13 -0.16 -0.15 -0.12 -0.07 -0.04 -0.03 ## 1979-02-01 0.19 0.10 0.03 0.00 0.02 0.08 0.12 0.15 0.16 0.15 ## 1979-03-01 0.14 0.07 0.04 0.04 0.10 0.18 0.27 0.33 0.34 0.30 ## 1979-04-01 0.14 0.09 0.04 0.01 0.02 0.05 0.09 0.11 0.08 0.03 ## 1979-05-01 0.01 -0.02 -0.06 -0.08 -0.09 -0.06 -0.03 -0.02 -0.03 -0.05
\[\text{image} = \alpha_1 \lambda_1 + \alpha_2 \lambda_2 + \alpha_3 \lambda_3 + \dots\]
Pacific Decadal Oscillation index is an example. It is the weighting (\(\alpha\)) on the first \(\lambda\) from gridded SST anomalies in the North Pacific.
sst.pca <- prcomp(X_norm, scale = FALSE, center=FALSE)
sst.pca$rotation with each column an “eigen image”.sst.pca$x. One for each image and each \(\lambda\).Eigen images. Just first 10 pixels of the image are shown.
round(t(sst.pca$rotation)[1:5, 1:10], digits=2)
## p1 p2 p3 p4 p5 p6 p7 p8 p9 p10 ## PC1 0.06 0.06 0.05 0.05 0.04 0.04 0.04 0.04 0.04 0.03 ## PC2 -0.06 -0.06 -0.07 -0.07 -0.08 -0.08 -0.08 -0.07 -0.07 -0.07 ## PC3 0.02 0.01 0.00 -0.01 -0.02 -0.01 -0.01 0.00 0.01 0.01 ## PC4 -0.02 -0.02 -0.02 -0.02 -0.04 -0.05 -0.05 -0.06 -0.05 -0.04 ## PC5 0.00 0.01 0.02 0.02 0.03 0.03 0.03 0.03 0.03 0.04
# if image is say 25x25 library(raster) img <- as.raster(matrix(sst.pca$rotation[,1], byrow=TRUE, ncol=25)) plot(img)
## Warning: Currect projection of shape p_stack unknown. Long lat (epsg 4326) ## coordinates assumed.
\[\text{image} = \alpha_1 \lambda_1 + \alpha_2 \lambda_2 + \alpha_3 \lambda_3\]
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Use the first 10 dimensions.
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
* Wind and rain
* Strong upwelling starts from the tip and moves north